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Record W3203368576 · doi:10.3390/healthcare9101336

Reproductive Health Services: Attitudes and Practice of Japanese Community Pharmacists

2021· article· en· W3203368576 on OpenAlexaff
Shigeo Yamamura, Tomoko Terajima, Javiera Navarrete, Christine Hughes, Nesé Yuksel, Theresa J. Schindel, Tatta Sriboonruang, Puree Anantachoti, Chanthawat Patikorn

Bibliographic record

VenueHealthcare · 2021
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Contraception
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsReproductive healthFamily medicineReproductive medicineNursingMedicineMedical educationEnvironmental healthPregnancyPopulation

Abstract

fetched live from OpenAlex

The provision of sexual and reproductive health (SRH) services is an important part of a community pharmacist's role in many countries. However, such services are not traditionally provided by pharmacists in Japan. We surveyed the practice and attitudes regarding the provision of SRH services among Japanese community pharmacists with a focus on reproductive health (RH) topics. The participants were asked about the provision of RH services, attitudes toward their role as SRH providers, and self-reported confidence in providing education to patients on RH topics. We obtained 534 effective responses. About half of the participants reported providing RH services, and only 21% were involved in dispensing emergency contraception pills. Although the proportion of pharmacists providing education on these topics was considerably lower, about 80% recognized the importance of their role as SRH advisors. Confidence in providing patient education about RH topics depended on their experience in providing such services. Most participants were interested in additional SRH training (80%). Our results suggest that training programs could help to expand Japanese community pharmacists' roles as SRH providers and increase their confidence in the education of patients. This study provides useful insights to expand pharmacists' roles in Japan as providers of comprehensive SRH services.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.533
Threshold uncertainty score0.649

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.069
GPT teacher head0.427
Teacher spread0.359 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2021
Admission routes1
Has abstractyes

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